Background

Statistic Analysis

In the following section we will show the results for each fungicide

Macrophomina

Serial dilution

Analyses

##NORMALITY TEST-Shapiro_test

# shapiro.test.macrophomina <- macrophomina.xx %>%
#     do(tidy(shapiro.test(.$EC50)))
#   shapiro.test.macrophomina[[2]][[1]]
#   #surprisingly they are normal
#   
#   #
# AOV.1 <- (aov (EC50 ~ ID, data = macrophomina.xx))
# summary(AOV.1)
# #P value of 0.805, there is no difference 
# #ANOVA
#  macrophomina.xx %>% ggplot( aes(x= ID, y=EC50))+ geom_point() 
# ggsave("macrophomina.xx.png")

Sclerotium

Serial dilution

## Analyses

##NORMALITY TEST-Shapiro_test

shapiro.test.sclerotium <- sclerotium.yy %>%
    do(tidy(shapiro.test(.$EC50)))
  shapiro.test.sclerotium[[2]][[1]]
## [1] 0.0007752832
#No normals
  
  #NO NORMALILTY 0.00000125 , thats is why  krusKal
#by ID

object.1 <- kruskal.test(EC50 ~ ID, data = sclerotium.yy)
object.1[[3]][[1]]
## [1] 0.4731283
#There no is difference p-value = 0.4579297

#object.2 <-  DunnTest(EC50 ~ ID, data = sclerotium.yy, method = "bonferroni")
#object.2 

sclerotium.yy %>% ggplot( aes(x= ID, y=EC50)) + geom_point() + expand_limits( y = c(0, 27000)) +scale_y_continuous(
breaks = c(0, 500, 1000, 2000,  3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000))

ggsave("sclerotium.yy.png")
## Saving 7 x 5 in image